Abstract: Renewable energy sources such as wind and solar power are increasing in adoption. Historically, power has flowed from large power plants to customers. Increasing penetration of renewable energies such as solar power from photovoltaic rooftop installations has made the distribution network a two-way-street with power now being generated at the customer level. The incorporation of renewables introduces uncertainty and variability in the power grid which affects grid voltages. Distribution network operation studies are being adapted to include renewables; however, such studies require high-quality data on solar irradiance that adequately reflect realistic climatological and diurnal variabilities. Data from satellite-based products are spatially complete, but temporally coarse, whereas solar irradiances exhibit high-frequency variation at very fine timescales down to minutes. We propose a new stochastic downscaling method from satellite-based 30-minute snapshots of global horizontal irradiance to the one-minute resolution. The first step is a stochastic decision rule for distinguishing between clear and non-clear days. Solar irradiance’s first and second-order structures vary diurnally and seasonally, and our model adapts to such nonstationarity. Moreover, empirical irradiance data exhibits highly non-Gaussian behavior with heavier tails; we develop a nonstationary and non-Gaussian moving average model that is shown to capture realistic solar variability at multiple timescales in our data examples. We also propose a new estimation scheme based on Cholesky factors of empirical autocovariance matrices, bypassing difficult and inaccessible likelihood-based approaches. The approach is illustrated using the National Solar Radiation Database, as well as direct in situ measurements that are part of the University of Oregon’s Solar Radiation Monitoring Laboratory.